• Research in Community and Public Health Nursing
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• v.32 no.4
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• pp.430-444
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• 2021

Purpose: This study is an integrated literature review to analyze health problems and social determinants of multicultural adolescents in South Korea. Methods: An integrative review was conducted according to Whittemore & Knafl's guideline. An electronic search that included publications from 2018 to 2020 in the PubMed, EMBASE, Cochrane Library, CINAHL, RISS, and KISS databases was conducted. Of a total of 67 records that were identified, 13 finally met full inclusion criteria. Text network analysis was also conducted to identify keywords network trends using NetMiner program. Results: The health problems of multicultural adolescents were classified into mental health (depression, anxiety, suicide and acculturative stress) and health risk behaviors (smoking, risky drinking, smartphone dependence and sexual behavior). As social determinants affecting the health of multicultural adolescents, the biological factors such as gender, age, and visible minority, and the psychological factors such as acculturative stress, self-esteem, family support, and ego-resiliency were identified. The sociocultural factors were identified as family economic status, residential area, parental education level, and parents' country of birth. As a result of text network analysis, a total of 41 words were identified. Conclusion: Based on these results, mental health and health risk behaviors should be considered as interventions for health promotion of multicultural adolescents. Our findings suggest that further research should be conducted to broaden the scope of health determinants to account for the effects of the physical environment and health care system.

• The Korean journal of internal medicine
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• v.39 no.4
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• pp.577-589
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• 2024

Background/Aims: Four high-genetic barrier nucleos(t)ide analogues (NAs) for chronic hepatitis B (CHB), namely entecavir (ETV), tenofovir disoproxil fumarate (TDF), tenofovir alafenamide (TAF), and besifovir dipivoxil maleate (BSV), have been established. The aim of this study is to investigate the efficacy of four high-genetic barrier NAs using a network meta-analysis of randomized trials and propensity score-matched cohorts. Methods: Systematic search was performed using PubMed, Cochrane library, and EMBASE and included randomized controlled trials and cohort studies that used propensity score matching. Studies on treatment-naïve CHB patients treated with ETV, TDF, TAF, or BSV were included. Outcomes included alanine aminotransferase normalization and hepatitis B e antigen seroclearance at week 48 and undetectable hepatitis B virus DNA at weeks 48 and 96. Network meta-analysis was performed to synthesize the results. Results: In total, 15,000 patients from 16 studies were included. In terms of 48- and 96-week virologic response (VR), TDF outperformed ETV with statistical significance (48 weeks: odds ratio [OR], 1.38; p < 0.001; 96 weeks: OR, 1.57; p = 0.004). ETV was ranked first for 48-week biochemical response (BR) and outperformed TDF (OR, 0.76; p = 0.028). In the sensitivity analyses, 48-week VR from randomized-controlled trials were compiled, and the same trend toward the superiority of TDF over ETV was found (OR, 1.51; p = 0.030). Conclusions: Four high-genetic barrier NAs were compared, and TDF was more likely to achieve a VR after 48 weeks, while ETV provided a superior BR after 48 weeks.

• Journal of the Korean Society for information Management
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• v.32 no.1
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• pp.135-152
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• 2015

The goal of this paper is to explore the field of Computer and Information Science with the aid of text mining techniques by mining Computer and Information Science related conference data available in DBLP (Digital Bibliography & Library Project). Although studies based on bibliometric analysis are most prevalent in investigating dynamics of a research field, we attempt to understand dynamics of the field by utilizing Latent Dirichlet Allocation (LDA)-based multinomial topic modeling. For this study, we collect 236,170 documents from 353 conferences related to Computer and Information Science in DBLP. We aim to include conferences in the field of Computer and Information Science as broad as possible. We analyze topic modeling results along with datasets collected over the period of 2000 to 2011 including top authors per topic and top conferences per topic. We identify the following four different patterns in topic trends in the field of computer and information science during this period: growing (network related topics), shrinking (AI and data mining related topics), continuing (web, text mining information retrieval and database related topics), and fluctuating pattern (HCI, information system and multimedia system related topics).

• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.267-295
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• 2014

The purpose of this study is to analyze empirically relationships between learners' psychological factors, knowledge sourcing behavior and knowledge utilization outcomes and to analyze the mediation effect of social learning and relationships among learners. Another purpose is to understand learners' attitude on social learning and knowledge sourcing behavior. The main results of this study are as follows: First, regression results on relationships among learners' psychological factors, knowledge sourcing behavior, knowledge utilization outcomes show that learners' self-efficacy has a positive effect on social learning activity participation, and goal orientation has a positive influence on group knowledge sourcing and social learning activity participation. Users' experiences of social media has a positive effect on group knowledge sourcing, social learning activity participation and social learning interaction. From a knowledge utilization perspective, published knowledge sourcing positively affects knowledge reuse, knowledge application and knowledge innovation. Dyadic knowledge sourcing has positive influence on knowledge reuse. Group knowledge sourcing affects positively knowledge application and knowledge innovation. Second, social learning activity participation factor has full mediation effect on relationship between learners' goal orientation and group knowledge sourcing, and the relationship between users' experiences of social media and group knowledge sourcing. A relationship among members factor has full mediation effect on the relationship between published knowledge sourcing and knowledge reuse, and relationship between published knowledge sourcing and knowledge innovation. Third, the results of in-depth interview show that learners trust and easily collect knowledge from social network services in general. Also, they get a variety of idea for solving information problem from interaction among members in social learning community.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.75-90
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• 2011

This study aimed to investigate, analyze and identify the problems related to SNS usage by publishing companies around Twitter, Facebook and Me2day as well as theoretically investigate SNS, and ultimately to suggest the approaches to improve the outstanding issues identified. In accordance with the analysis, only 0.5%(222 publishing companies) in total publishing companies(41,407) opened SNS[3.7%(1,537) opened the independent website.] Second, in accordance with the investigation on 212 publishing companies in 222 companies opening SNS(71 twitters, 74 facebooks, 67 Me2days), the communication was not significant to the extent that only 50.5%(107) kept the communication with less than 100 readers or potential readers. Furthermore, 77.4%(164 companies) had less than 1,000 postings by publishing companies. The analysis on the postings(500) by users and postings(300) by publishing companies demonstrated that those postings were mostly related to marketing, introduction, recommendation and reading of publications by publishing companies. It means that the postings were mostly positive. However, 86.6% of postings by users in SNS of publishing companies was merely one-time posting. It indicated that continuity was not sufficient.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.181-203
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• 2022

This study analyzed domestic and foreign literature and examined cases of foreign Community of Practice(CoP) of experts to find out what benefits researchers can gain from participating in their CoP, how the CoP was established, and how data is shared within the CoP. In addition, this study discussed on how to establish a CoP of experts in Korea for sharing and using research data. By participating in the CoP of experts, members can be provided with the opportunity to build an experts' network and have a chance to meet with various experts, to acquire and share their expertise and information, to receive help from other experts, to learn about their expertise, and to have opportunities for professional experiences. In addition, this study discussed 4 factors such as operation method and management system, memberships and number of members, activities, and management of data and repository for establishing a CoP of experts for sharing and using research data. This study provides a knowledge base for building a CoP of experts in Korea.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.135-161
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• 2023

This study analyzed the research trends of COVID-19 research papers published in medical journals of Korea. Data were collected from 25 MEDLINE journals in 'Medicine and Pharmacy' studies and a total of 800 were selected. As a result of the study, authors from domestic affiliations made up 76.96% of the total, and the proportion of authors from foreign institutions decreased without significant change. The authors' majors were 'Internal Medicine' (32.85%), 'Preventive Medicine/Occupational and Environmental Medicine' (16.23%), 'Radiology' (5.74%), and 'Pediatrics' (5.50%), and 435 (54.38%) papers were collaborative research. As for author keywords, 'COVID19' (674), 'SARSCoV2' (245), 'Coronavirus' (81), and 'Vaccine' (80) were derived as top keywords. There were six words that appeared throughout the entire period: 'COVID19,' 'SARSCoV2,' 'Coronavirus,' 'Korea,' 'Pandemic,' and 'Mortality.' Co-occurrence network analysis was conducted on MeSH terms and author keywords, and common keywords such as 'covid-19,' 'sars-cov-2,' and 'public health' were derived. In topic modeling, five topics were identified, including 'Vaccination,' 'COVID-19 outbreak status,' 'Omicron variant,' 'Mental health, control measures,' and 'Transmission and control in Korea.' Through this study, it was possible to identify the research areas and major keywords by year of COVID-19 research papers published during the 'Public Health Emergency of International Concern (PHEIC).'

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.99-118
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• 2014

This paper carried out co-word analysis of noun and noun phrase using text-mining technique in order to grasp the research trends on domestic and international information science articles. It was conducted based on collected titles and articles of the papers published in the Journal of the Korean Society for Information Management (KOSIM) and Journal of American Society for Information Science and Technology (JASIST) from 1990 to 2013. By dividing whole period into five publication window, this paper was organized into the following processes: 1) analysis of high frequency co-word pair to examine the overall trends of both information science articles 2) analysis of each word appearing with high frequency keyword to grasp the detailed subject 3) focused network analysis of trend after 2010 when distinctively new keyword appeared. The result of the analysis shows that KOSIM has considerable portion of studies conducted regarding topics such as library, information service, information user and information organization. Whereas, JASIST has focused on studies regarding information retrieval, information user, web information, and bibliometrics.